Scala 删除存在时间戳列问题的常量列_Scala_Apache Spark_Apache Spark Sql_Apache Spark Mllib

Scala 删除存在时间戳列问题的常量列

scala apache-spark

Scala 删除存在时间戳列问题的常量列,scala,apache-spark,apache-spark-sql,apache-spark-mllib,Scala,Apache Spark,Apache Spark Sql,Apache Spark Mllib,嗨，伙计们，我做了这段代码，允许删除具有常量值的列。我首先计算标准偏差，然后去掉标准值等于零的，但是当有一个列有一个时间戳类型时，我遇到了这个问题由于数据类型不匹配，无法解析“stddev_samp（time.1）”：参数1需要双精度类型，但“time.1”是时间戳类型 val spark = SparkSession.builder.master("local").appName("my-spark-app").getOrCreate() //val df = spark.range(1,

嗨，伙计们，我做了这段代码，允许删除具有常量值的列。我首先计算标准偏差，然后去掉标准值等于零的，但是当有一个列有一个时间戳类型时，我遇到了这个问题

由于数据类型不匹配，无法解析“stddev_samp（

time.1

）”：参数1需要双精度类型，但“

time.1

”是时间戳类型

val spark = SparkSession.builder.master("local").appName("my-spark-app").getOrCreate()
//val df = spark.range(1, 1000).withColumn("X2", lit(0)).toDF("X1","X2")
val df = spark.read.option("inferSchema", "true").option("header", "true").csv("C:/Users/mhattabi/Desktop/dataTestCsvFile/dataTest2.txt")
df.show(5)
//df.columns.map(p=>s"`${p}`")
//val aggs = df.columns.map(c => stddev(c).as(c)) 
val aggs = df.columns.map(p=>stddev(s"`${p}`").as(p))
val stddevs = df.select(aggs: _*)
val columnsToKeep: Seq[Column] = stddevs.first  // Take first row
  .toSeq  // convert to Seq[Any]
  .zip(df.columns)  // zip with column names
  .collect {
    // keep only names where stddev != 0
    case (s: Double, c) if s != 0.0  => col(c) 
  }
df.select(columnsToKeep: _*).show(5,false)

使用

stddev

stddev

仅在数字列上定义。如果要计算

日期

列的标准偏差，需要先将其转换为时间戳：

scala> var myDF = (0 to 10).map(x => (x, scala.util.Random.nextDouble)).toDF("id", "rand_double")
myDF: org.apache.spark.sql.DataFrame = [id: int, rand_double: double]

scala> myDF = myDF.withColumn("Date", current_date())
myDF: org.apache.spark.sql.DataFrame = [id: int, rand_double: double ... 1 more field]

scala> myDF.printSchema
root
 |-- id: integer (nullable = false)
 |-- rand_double: double (nullable = false)
 |-- Date: date (nullable = false)


scala> myDF.show
+---+-------------------+----------+
| id|        rand_double|      Date|
+---+-------------------+----------+
|  0| 0.3786008989478248|2017-03-21|
|  1| 0.5968932024004612|2017-03-21|
|  2|0.05912760417456575|2017-03-21|
|  3|0.29974600653895667|2017-03-21|
|  4| 0.8448407414817856|2017-03-21|
|  5| 0.2049495659443249|2017-03-21|
|  6| 0.4184846380144779|2017-03-21|
|  7|0.21400484330739022|2017-03-21|
|  8| 0.9558142791013501|2017-03-21|
|  9|0.32530639391058036|2017-03-21|
| 10| 0.5100585655062743|2017-03-21|
+---+-------------------+----------+

scala> myDF = myDF.withColumn("Date", unix_timestamp($"Date"))
myDF: org.apache.spark.sql.DataFrame = [id: int, rand_double: double ... 1 more field]

scala> myDF.printSchema
root
 |-- id: integer (nullable = false)
 |-- rand_double: double (nullable = false)
 |-- Date: long (nullable = true)


scala> myDF.show
+---+-------------------+----------+
| id|        rand_double|      Date|
+---+-------------------+----------+
|  0| 0.3786008989478248|1490072400|
|  1| 0.5968932024004612|1490072400|
|  2|0.05912760417456575|1490072400|
|  3|0.29974600653895667|1490072400|
|  4| 0.8448407414817856|1490072400|
|  5| 0.2049495659443249|1490072400|
|  6| 0.4184846380144779|1490072400|
|  7|0.21400484330739022|1490072400|
|  8| 0.9558142791013501|1490072400|
|  9|0.32530639391058036|1490072400|
| 10| 0.5100585655062743|1490072400|
+---+-------------------+----------+

此时，所有列都是数字，因此代码将正常运行：

scala> :pa
// Entering paste mode (ctrl-D to finish)

val aggs = myDF.columns.map(p=>stddev(s"`${p}`").as(p))
val stddevs = myDF.select(aggs: _*)
val columnsToKeep: Seq[Column] = stddevs.first  // Take first row
  .toSeq  // convert to Seq[Any]
  .zip(myDF.columns)  // zip with column names
  .collect {
    // keep only names where stddev != 0
    case (s: Double, c) if s != 0.0  => col(c)
  }
myDF.select(columnsToKeep: _*).show(false)

// Exiting paste mode, now interpreting.

+---+-------------------+
|id |rand_double        |
+---+-------------------+
|0  |0.3786008989478248 |
|1  |0.5968932024004612 |
|2  |0.05912760417456575|
|3  |0.29974600653895667|
|4  |0.8448407414817856 |
|5  |0.2049495659443249 |
|6  |0.4184846380144779 |
|7  |0.21400484330739022|
|8  |0.9558142791013501 |
|9  |0.32530639391058036|
|10 |0.5100585655062743 |
+---+-------------------+

aggs: Array[org.apache.spark.sql.Column] = Array(stddev_samp(id) AS `id`, stddev_samp(rand_double) AS `rand_double`, stddev_samp(Date) AS `Date`)
stddevs: org.apache.spark.sql.DataFrame = [id: double, rand_double: double ... 1 more field]
columnsToKeep: Seq[org.apache.spark.sql.Column] = ArrayBuffer(id, rand_double)

使用

countDistinct

尽管如此，使用

countDistinct

更为普遍：

scala> val distCounts = myDF.select(myDF.columns.map(c => countDistinct(c) as c): _*).first.toSeq.zip(myDF.columns)
distCounts: Seq[(Any, String)] = ArrayBuffer((11,id), (11,rand_double), (1,Date))]

scala> distCounts.foldLeft(myDF)((accDF, dc_col) => if (dc_col._1 == 1) accDF.drop(dc_col._2) else accDF).show
+---+-------------------+
| id|        rand_double|
+---+-------------------+
|  0| 0.3786008989478248|
|  1| 0.5968932024004612|
|  2|0.05912760417456575|
|  3|0.29974600653895667|
|  4| 0.8448407414817856|
|  5| 0.2049495659443249|
|  6| 0.4184846380144779|
|  7|0.21400484330739022|
|  8| 0.9558142791013501|
|  9|0.32530639391058036|
| 10| 0.5100585655062743|
+---+-------------------+

使用

stddev

stddev

仅在数字列上定义。如果要计算

日期

列的标准偏差，需要先将其转换为时间戳：

scala> var myDF = (0 to 10).map(x => (x, scala.util.Random.nextDouble)).toDF("id", "rand_double")
myDF: org.apache.spark.sql.DataFrame = [id: int, rand_double: double]

scala> myDF = myDF.withColumn("Date", current_date())
myDF: org.apache.spark.sql.DataFrame = [id: int, rand_double: double ... 1 more field]

scala> myDF.printSchema
root
 |-- id: integer (nullable = false)
 |-- rand_double: double (nullable = false)
 |-- Date: date (nullable = false)


scala> myDF.show
+---+-------------------+----------+
| id|        rand_double|      Date|
+---+-------------------+----------+
|  0| 0.3786008989478248|2017-03-21|
|  1| 0.5968932024004612|2017-03-21|
|  2|0.05912760417456575|2017-03-21|
|  3|0.29974600653895667|2017-03-21|
|  4| 0.8448407414817856|2017-03-21|
|  5| 0.2049495659443249|2017-03-21|
|  6| 0.4184846380144779|2017-03-21|
|  7|0.21400484330739022|2017-03-21|
|  8| 0.9558142791013501|2017-03-21|
|  9|0.32530639391058036|2017-03-21|
| 10| 0.5100585655062743|2017-03-21|
+---+-------------------+----------+

scala> myDF = myDF.withColumn("Date", unix_timestamp($"Date"))
myDF: org.apache.spark.sql.DataFrame = [id: int, rand_double: double ... 1 more field]

scala> myDF.printSchema
root
 |-- id: integer (nullable = false)
 |-- rand_double: double (nullable = false)
 |-- Date: long (nullable = true)


scala> myDF.show
+---+-------------------+----------+
| id|        rand_double|      Date|
+---+-------------------+----------+
|  0| 0.3786008989478248|1490072400|
|  1| 0.5968932024004612|1490072400|
|  2|0.05912760417456575|1490072400|
|  3|0.29974600653895667|1490072400|
|  4| 0.8448407414817856|1490072400|
|  5| 0.2049495659443249|1490072400|
|  6| 0.4184846380144779|1490072400|
|  7|0.21400484330739022|1490072400|
|  8| 0.9558142791013501|1490072400|
|  9|0.32530639391058036|1490072400|
| 10| 0.5100585655062743|1490072400|
+---+-------------------+----------+

此时，所有列都是数字，因此代码将正常运行：

scala> :pa
// Entering paste mode (ctrl-D to finish)

val aggs = myDF.columns.map(p=>stddev(s"`${p}`").as(p))
val stddevs = myDF.select(aggs: _*)
val columnsToKeep: Seq[Column] = stddevs.first  // Take first row
  .toSeq  // convert to Seq[Any]
  .zip(myDF.columns)  // zip with column names
  .collect {
    // keep only names where stddev != 0
    case (s: Double, c) if s != 0.0  => col(c)
  }
myDF.select(columnsToKeep: _*).show(false)

// Exiting paste mode, now interpreting.

+---+-------------------+
|id |rand_double        |
+---+-------------------+
|0  |0.3786008989478248 |
|1  |0.5968932024004612 |
|2  |0.05912760417456575|
|3  |0.29974600653895667|
|4  |0.8448407414817856 |
|5  |0.2049495659443249 |
|6  |0.4184846380144779 |
|7  |0.21400484330739022|
|8  |0.9558142791013501 |
|9  |0.32530639391058036|
|10 |0.5100585655062743 |
+---+-------------------+

aggs: Array[org.apache.spark.sql.Column] = Array(stddev_samp(id) AS `id`, stddev_samp(rand_double) AS `rand_double`, stddev_samp(Date) AS `Date`)
stddevs: org.apache.spark.sql.DataFrame = [id: double, rand_double: double ... 1 more field]
columnsToKeep: Seq[org.apache.spark.sql.Column] = ArrayBuffer(id, rand_double)

使用

countDistinct

尽管如此，使用

countDistinct

更为普遍：

scala> val distCounts = myDF.select(myDF.columns.map(c => countDistinct(c) as c): _*).first.toSeq.zip(myDF.columns)
distCounts: Seq[(Any, String)] = ArrayBuffer((11,id), (11,rand_double), (1,Date))]

scala> distCounts.foldLeft(myDF)((accDF, dc_col) => if (dc_col._1 == 1) accDF.drop(dc_col._2) else accDF).show
+---+-------------------+
| id|        rand_double|
+---+-------------------+
|  0| 0.3786008989478248|
|  1| 0.5968932024004612|
|  2|0.05912760417456575|
|  3|0.29974600653895667|
|  4| 0.8448407414817856|
|  5| 0.2049495659443249|
|  6| 0.4184846380144779|
|  7|0.21400484330739022|
|  8| 0.9558142791013501|
|  9|0.32530639391058036|
| 10| 0.5100585655062743|
+---+-------------------+